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Original citation: Navaud, Olivier, Barbacci, Adelin, Taylor, Andrew, Clarkson, John P. and Raffaele, Sylvain (2018) Shifts in diversification rates and host jump frequencies shaped the diversity of host range among fungal pathogens. Molecular Ecology . doi:10.1111/mec.14523

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Received: 30 May 2017 | Revised: 26 January 2018 | Accepted: 29 January 2018 DOI: 10.1111/mec.14523

ORIGINAL ARTICLE

Shifts in diversification rates and host jump frequencies shaped the diversity of host range among Sclerotiniaceae fungal plant pathogens

Olivier Navaud1 | Adelin Barbacci1 | Andrew Taylor2 | John P. Clarkson2 | Sylvain Raffaele1

1LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan, France Abstract 2Warwick Crop Centre, School of Life The range of hosts that a parasite can infect in nature is a trait determined by its Sciences, University of Warwick, Coventry, own evolutionary history and that of its potential hosts. However, knowledge on UK host range diversity and evolution at the family level is often lacking. Here, we Correspondence investigate host range variation and diversification trends within the Sclerotiniaceae, Sylvain Raffaele, LIPM, Universitede Toulouse, INRA, CNRS, Castanet-Tolosan, a family of Ascomycete fungi. Using a phylogenetic framework, we associate diversi- France. fication rates, the frequency of host jump events and host range variation during Email:[email protected] the evolution of this family. Variations in diversification rate during the evolution of Funding information the Sclerotiniaceae define three major macro-evolutionary regimes with contrasted European Research Council, Grant/Award Number: ERC-StG-336808; the French proportions of species infecting a broad range of hosts. Host–parasite cophyloge- Laboratory of Excellence project TULIP, netic analyses pointed towards parasite radiation on distant hosts long after host Grant/Award Number: ANR-10-LABX-41, ANR-11-IDEX-0002-02 speciation (host jump or duplication events) as the dominant mode of association with in the Sclerotiniaceae. The intermediate macro-evolutionary regime showed a low diversification rate, high frequency of duplication events and the highest proportion of broad host range species. Our findings suggest that the emer- gence of broad host range fungal pathogens results largely from host jumps, as pre- viously reported for oomycete parasites, probably combined with low speciation rates. These results have important implications for our understanding of fungal par- asites evolution and are of particular relevance for the durable management of dis- ease epidemics.

KEYWORDS angiosperms, coevolution, fungi, host parasite interactions

1 | INTRODUCTION infect more than a hundred unrelated host species (Barrett, Kniskern, Bodenhausen, Zhang, & Bergelson, 2009; Woolhouse, Taylor, & Hay- The host range of a parasite has a central influence on the emergence don, 2001). Host specialization, when lineages evolve to infect a nar- and spread of disease (Woolhouse & Gowtage-Sequeria, 2005). There rower range of hosts than related lineages, is a frequent occurrence in is a clear demarcation between specialist parasites that can only infect living systems and can be driven by a parasite sharing habitat with only one or a few closely related host species, and generalists that can a limited number of potential hosts. There are also clear examples of

------This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2018 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd | Molecular Ecology. 2018;1–15. wileyonlinelibrary.com/journal/mec 1 2 | NAVAUD ET AL. parasite adaptations that restrict the use of co-occurring potential how fungal host range has changed over time. The relationship hosts. For instance, the red rust fungal pathogen Coleosporium ipo- between host range and diversification rates is an active area of moeae infects fewer Ipomoea species from its native community than research in insect ecology (Hamm & Fordyce, 2015; Hardy & Otto, when inoculated to non-native communities, implying that evolution 2014). For instance, a positive correlation was reported between spe- within local communities has narrowed pathogen host range (Chappell cies richness in the butterfly family Nymphalidae and the diversity of & Rausher, 2016). Some isolates of the rice blast Magnaporthe plants they feed upon (Janz, Nylin, & Wahlberg, 2006). However in oryzae are able to infect Oryza sativa japonica varieties but not indica contrast, some studies found a negative relationship between host- varieties co-occurring in the Yuanyang area of China, because their plant breadth and diversification rate (Hardy & Otto, 2014). Transi- genome harbours numerous avirulence effector genes (Liao et al., tions between feeding strategies generally appeared to be associated 2016). It has been proposed that specialization results from trade-offs with shifts in insect diversification rates (Hardy & Otto, 2014; Janz & between traits needed to infect a wide range of hosts (Futuyma & Nylin, 2008). Estimating fungal species divergence times in the Sclero- Moreno, 1988; Joshi & Thompson, 1995). It is frequently associated tiniaceae will allow testing of whether biological diversification is with the loss of traits that are not required to infect a particular host, related to host range variation. such as the loss of lipid synthesis in parasitoid wasps (Visser et al., Host range is a trait determined not only by the evolutionary his- 2010), and the loss of secondary metabolism and carbohydrate active tory of a parasite, but also by that of its potential hosts (Poulin & enzymes in powdery mildew fungal pathogens (Spanu et al., 2010). Keeney, 2008). Accounting for host association patterns should there- Specialization is sometimes considered as an evolutionary dead end fore prove useful to understand host range evolution in the Sclerotini- (Moran, 1988), as gene losses are often irreversible and may lead spe- aceae. The Leotiomycete class diverged less than 200 million years cialist lineages “down a blind alley” that limits transitions back to gen- ago (Beimforde et al., 2014; Prieto & Wedin, 2013) and likely radiated eralism (Day, Hua, & Bromham, 2016; Haldane, 1951). There is with the diversification of flowering plants (Smith, Beaulieu, & Dono- nevertheless evidence for transitions from specialist to generalist para- ghue, 2010). Molecular phylogenetic studies distinguished Myriosclero- sitism (Hu et al., 2014; Johnson, Malenke, & Clayton, 2009). For tinia, sensu stricto, and , and instance in plant pathogens, Pseudoperonospora cubensis differs from sensu stricto as monophyletic clades within the Sclerotiniaceae family other downy mildew oomycete pathogens in that it is able to infect a (Holst-Jensen, Vaage, & Schumacher, 1998; Holst-Jensen, Vralstad, & wide range of Cucurbits (Thines & Choi, 2015). For many parasite lin- Schumacher, 2004). A phylogenetic analysis of Monilinia species sug- eages however, knowledge on host range diversity and evolution at gested that cospeciation with host plants was the dominant pattern in the macro-evolutionary level is lacking. this clade (Holst-Jensen, Kohn, Jakobsen, & Schumacher, 1997). By Sclerotiniaceae is a family of Ascomycete fungi of the class Leo- contrast, there was no evidence for cospeciation with host plants in a tiomycetes which includes numerous plant parasites. Among the most phylogenetic analysis of Botrytis species (Staats, Van Baarlen, & Van studied are the grey mould pathogen , considered to be Kan, 2005). Botrytis species were thus proposed to have evolved one of the 10 most devastating plant pathogens (Dean et al., 2012), through host jumps to unrelated host plants followed by adaptation to and the white and stem mould pathogen . Both their new hosts (Dong, Raffaele, & Kamoun, 2015; Staats et al., 2005). are economically important pathogens in agriculture that infect hun- Host–parasite cophylogenetic analyses are required to test whether dreds of host plant species (Bolton, Thomma, & Nelson, 2006; Mben- variations in the frequency of host jumps may have impacted on host gue et al., 2016). The Sclerotiniaceae family also includes host range variation in the Sclerotinaceae. specialist parasites such as camelliae that causes flower blight In this study, we performed a phylogenetic analysis on 105 Sclero- on Camellia (Denton-Giles, Bradshaw, & Dijkwel, 2013), Sclerotinia gla- tiniaceae species to reveal multiple independent shifts and expansions cialis that specifically infects glacialis (Graf & Schumacher, of host range. We show that three macro-evolutionary regimes with 1995), and causing the cottonball disease on cran- distinct diversification rates and dominant host association patterns berry (McManus, Best, & Voland, 1999). Other species from the Sclero- have shaped the diversity of the Sclerotiniaceae and lead to contrasted tiniaceae have intermediate host range (tens of plant species) such as proportions of broad host range species. Specifically, we highlight an , S. subarctica and S. borealis (Clarkson, Carter, & increased emergence of broad host range parasites during the transi- Coventry, 2010; Farr & Rossman, 2016). While S. sclerotiorum and tion between macro-evolutionary regimes dominated by distinct pat- B. cinerea are considered as typical necrotrophic pathogens, rapidly terns of host–pathogen association. These results suggest that killing host cells to cause disease, the Sclerotiniaceae include species reduced diversification rates and high host jump frequency could asso- with diverse lifestyles. For instance, the poplar pathogen Ciborinia ciate with the emergence of generalist pathogens. whetzelii and several species are biotrophs that can live as endophytes (Andrew, Barua, Short, & Kohn, 2012; Schumacher & 2 | MATERIALS AND METHODS Kohn, 1985), while Coprotinia minutula is coprophilous (Elliott, 1967). How this remarkable diversity evolved remains elusive. To gain 2.1 | Taxon and host range data selection insights into this question, knowledge of phylogenetic relationships and host range diversity at the macro-evolutionary level is needed. We used all 105 Sclerotiniaceae species for which at least ITS marker Specifically, ancestral state reconstruction can provide insights into sequence data were available in the GenBank database. As outgroups NAVAUD ET AL. | 3 we selected 56 Rutstroemiaceae species and 39 representative species Binary MCMC) and BayArea methods to verify congruence between of and Sordariomycetes for a total of 200 species. Host the methods and assess the robustness of their output, as recom- range data were obtained from (Boland & Hall, 1994; Melzer, Smith, & mended by (Yu et al., 2015). We report the results of the S-DIVA Boland, 1997), Index Fungorum (http://www.indexfungorum.org), the analysis which is considered the best adapted for host–parasite asso- SMML fungus-host distribution database (Farr & Rossman, 2016) and ciation analyses (Razo-Mendivil & De Leon, 2011; Yu et al., 2015). references therein. In total, we retrieved 7,101 fungus-host associa- To implement S-DIVA, we delimited host groups as follows: Vitales tion records that did not show strong geographic or crop/wild species (A), Asterids (B), Campanuliids (C), Commelinids (D), coprophilous (E), bias (Figure S1). Fungal host range was not correlated to the number core (F), Eudicots (G), Fabids (H), Polypodiidae (I), Lamiids of database records (Figure S1c). Calibrated deciphering the rela- (J), Magnoloidae (K), Malvids (L), Monocotyledones (M), Pinidae (N), tionships between the host families based on seven gene regions (18S allowing a maximum of four groups at each node. Among these rDNA, 26S rDNA, ITS, matK, rbcL, atpB and trnL-F) were extracted groups, the latest divergence is that of Malvids and Fabids, esti- from (Qian & Zhang, 2014) and updated with (Hedges, Marin, Suleski, mated around 82.8 and 127.2 Mya (Clarke, Warnock, & Donoghue, Paymer, & Kumar, 2015). The of host families used for cophyloge- 2011) and predates the emergence of Sclerotiniaceae estimated at netic analyses is provided as File S1. ~69.7 Mya in this work; we therefore did not restrict associations in the ancestral reconstruction analysis. To account for incomplete sampling in this analysis, ancestral state reconstruction was com- 2.2 | Initial phylogenetic analysis puted for every plant group by the re-rooting method (Yang, Kumar,

ITS sequences were aligned using MAFFT version 7 (Katoh & Standley, & Nei, 1995) under an entity-relationship (ER) model available in PHY-

2013). The alignment was manually adjusted to minimize possible TOOLS (Revell, 2012) and derived from the ape library in R (Paradis, homoplasic positions using Seaview 4 (Gouy, Guindon, & Gascuel, Claude, & Strimmer, 2004). This method, based on maximum likeli- 2010). We retained gaps shorter than 41 positions present in a max- hood, computed the probability to infect a given group of hosts for imum of 39 sequences, with ungapped blocks being at least five every nodes of the Sclerotiniaceae phylogeny. We considered plant characters long with a maximum of 5% ambiguous nucleotides per groups as hosts when the probability of the ancestral state was position. Unalignable and autapomorphic regions were excluded from >50%. For every plant group, up to 10% of terminal nodes were the analysis, yielding an alignment with 797 informative sites pruned randomly 100 times and the ancestral state reconstructed on (File S2). Maximum-likelihood phylogeny was inferred with PhyML 3 pruned trees. For each plant group, we then extracted the variation (Guindon et al., 2010) with Smart Model Selection which permits an in the age of the most ancestral inclusion into the Sclerotiniaceae automatic substitution model selection supporting the general time- host range (Figure S2). For all plant groups, this variation was not reversible model with gamma distribution using six substitution rate significantly different from 0, indicating that ancestral state recon- categories (GTR+G6) model as the best fit. Statistical branch support struction was robust to tree pruning. was inferred with the SH-like approximate likelihood ratio test (SH- aLRT) (Anisimova, Gil, Dufayard, Dessimoz, & Gascuel, 2011) and 2.4 | Divergence dating analyses bootstrap analysis with 100 replicates (Files 3 and 4). The topology of the tree was confirmed by three additional methods. First, we We used a Bayesian approach to construct a chronogram with abso- used a neighbour-joining approach in FastME 2.0 (Lefort, Desper, & lute times with the program BEAST 1.8.2. As no fossil is known in this Gascuel, 2015) with the LogDet substitution model and tree refine- fungal group, we use Sordariomycetes–Leotiomycetes divergence ment by subtree pruning and regrafting (File S5). Second, we used time to calibrate the tree (~300 Mya, Beimforde et al., 2014). Using the parsimony ratchet approach implemented in the PHANGORN pack- our initial phylogeny as a constraint, the partition file was prepared age in R (Schliep, 2011; File S6). Third, we used Bayesian analysis in with the BEAUti application of the BEAST package. Considering that MrBayes 3.2.6 (Ronquist et al., 2012) using the GTR substitution our data set includes distantly related taxa, subsequent analyses model with gamma-distributed rate variation across sites and a pro- were carried out using an uncorrelated relaxed clock model with log- portion of invariable sites in 100 million MCMC generations, sam- normal distribution of rates. We used a uniform birth–death model pling parameters every 1,000, removing 10% of the tree files as with incomplete species sampling as prior on node age, following burn-in. The resulting trees, available in Dryad Digital Repository (Beimforde et al., 2014) to account for incomplete sampling in our (https://doi.org/10.5061/dryad.7cs3g), were edited with Figtree phylogeny. Analyses were run three times for 100 million genera- (available at http://tree.bio.ed.ac.uk/software/figtree/). tions, sampling parameters every 5,000 generations, assessing con- vergence and sufficient chain mixing using Tracer 1.6 (Drummond, Suchard, Xie, & Rambaut, 2012). We removed 20% of trees as burn- 2.3 | Ancestral state reconstruction in, and the remaining trees were combined using LogCombiner (BEAST To infer possible ancestral hosts, we used Reconstruct Ancestral package), and summarized as maximum clade credibility (MCC) trees

State in Phylogenies 3.1 (RASP) (Yu, Harris, Blair, & He, 2015). We using TreeAnnotator within the BEAST package. The trees were edited used the S-DIVA (Statistical Dispersal-Vicariance Analysis), S-DEC in FigTree. The R packages ape (Paradis et al., 2004) and PHYTOOLS (Statistical Dispersal–Extinction–Cladogenesis model), BBM (Bayesian (Revell, 2012) were used for subsequent analyses. 4 | NAVAUD ET AL.

2.5 | Diversification analysis 2.6 | Cophylogeny analysis

We used the section containing all 105 Sclerotiniaceae species of To detect potential codivergence patterns, we tested for cophylogeny the chronogram with absolute times generated by BEAST to perform between fungal species and host family trees with CoRe-PA 0.5.1 diversification analyses with three different methods. We first used (Merkle, Middendorf, & Wieseke, 2010), PACo (Balbuena, Mıguez- Bayesian Analysis of Macroevolutionary Mixtures (BAMM) version Lozano, & Blasco-Costa, 2013) and Jane 4 (Conow, Fielder, Ovadia, & 2.5 (Rabosky et al., 2013, 2014), an application designed to account Libeskind-Hadas, 2010) as these programs can accommodate patho- for variation in evolutionary rates over time and among lineages. gens with multiple hosts. The only three species which were not able The priors were set using the setBAMMPriors command in the to interact with an angiosperm host (saprotroph or only described on

BAMMTOOLS R-package (Rabosky et al., 2014). We ran four parallel gymnosperms) were excluded from the analysis (namely: Elliottinia Markov chains for 5,000,000 generations and sampled for every kerneri, Coprotinia minutula and cryptomeriae). We used a 1,000 trees. The output and subsequent analyses were conducted full set of 263 host–pathogen associations and a simplified set of 121 with BAMMTOOLS. We discarded the first 10% of the results and associations minimizing the number of host families involved to con- checked for convergence and the effective sample size (ESS) using trol for the impact of sampling bias (Table 1). We also performed the the coda R-package. Lineage-through-time plots with extant and analyses independently for each macro-evolutionary regime. To esti- extinct lineages were computed using the PHYTOOLS package in R mate cost parameters in CoRe-PA, we ran a first cophylogeny recon- (Revell, 2012) with the drop.extinct=FALSE parameter. The exact struction with cost values calculated automatically using the simplex number of Sclerotiniaceae species is currently unknown, and no method on 1,000 random cycles, with all host switches permitted, intentional bias was introduced in data collection (Figure S1). To direct host switch and full host switch permitted (other parameters detect shifts in diversification rates taking into account incomplete set to CoRe-PA default). The best reconstruction had a quality score sampling of the Sclerotiniaceae diversity and tree uncertainty, we of 1.348 with total costs 37.076 and calculated costs of 0.0135 for used MEDUSA, a stepwise approach based upon the Akaike infor- cospeciation, 0.1446 for sorting, 0.2501 for duplication and 0.5918 mation criterion (AIC) (Alfaro et al., 2009; Drummond, Eastwood, for host switch. With these costs and parameters, we then computed Miotto, & Hughes, 2012). For this analysis, we used the birth and reconstruction with 1,000 random associations to test for the robust- death (bd) model, allowed rate shifts both at stem and nodes, used ness of the reconstructions. Next, we used 100,000 permutations of the AIC as a statistical criterion with initial r = .05 and e = 0.5. the host–pathogen association matrix in PACo to test for the overall Sampling of the fungal diversity is typically estimated around 5% congruence between host and pathogen trees. To test for the contri- (Hawksworth & Luecking, 2017). We used random sampling rates bution of each association on the global fit, we performed taxon jack- between 5% and 100% for each individual species in 100 MEDUSA knifing. We used one-tailed z tests to compare squared residuals bootstrap replicates to estimate the impact of sampling richness. distribution for each host–pathogen link with the median of squared Next, we randomly pruned 1% to 10% of species from the tree in residuals for the whole tree and infer likely co-evolutionary and host 100 MEDUSA bootstrap replicates to estimate the impact of tree shift links at p < .01. We used optimal costs calculated by CoRe-PA completeness. To control for the impact of divergence time esti- to run cophylogeny reconstruction in Jane 4 Solve Mode with 50 mates on the diversification analysis, we altered all branching times generations and a population size of 1,000. Statistical tests were per- in the tree randomly by 15% to +15% in 100 MEDUSA bootstrap formed using 100 random tip mapping with 20 generations and popu- replicates (Figure S3). Finally, we used RPANDA which includes lation size of 100. For the analysis of each macro-evolutionary regime model-free comparative methods for evolutionary analyses (Morlon in Jane 4, we used simplified host–pathogen association set to reduce et al., 2016). The model-free approach in RPANDA compares phyloge- the amount of Losses and Failure to Diverge predicted and facilitate netic tree shapes based on spectral graph theory (Lewitus & Mor- comparisons with CoRe-PA and PACo results. We visualized the lon, 2015). It constructs the modified Laplacian graph of a interactions with TreeMap3 (Charleston & Robertson, 2002), using phylogenetic tree, a matrix with eigenvalues reflecting the connec- the untangling function to improve the layout. tivity of the tree. The density profile of eigenvalues (Figure S4a) provides information on the entire tree structure. The algorithm 3 | RESULTS next used k-medioids clustering to identify eight modalities in the phylogenetic tree of the Sclerotiniaceae (Figure S4b,c). A post hoc 3.1 | Multiple independent shifts and expansions of test comparing Bayesian information criterion (BIC) values for ran- host range in the evolution of the Sclerotiniaceae domly bifurcated trees (BICrandom) with that of the tree of the

Sclerotiniaceae (BICSclerotiniaceae) supported the eight modalities To document the extant diversity in the Sclerotiniaceae fungi, we col-

(BICrandom/BICSclerotiniaceae = 10.34, well above the significance lected information on the host range of 105 species in this family. threshold of 4.0). We obtained significant BIC ratios with two or For comparison purposes, we also analysed the host range of 56 more modalities, supporting the existence of at least two macro- species from the Rutstroemiaceae family, a sister group of the Sclero- evolutionary regimes in the Sclerotiniaceae. tiniaceae (Figure 1a, Table S1). To reduce biases that may arise due NAVAUD ET AL. | 5 to missing infection reports, we analysed host range at the family Sclerotiniaceae species (10.4%) exhibited this trait, including Botrytis level instead of genus or species level. We found one species in the cinerea, Sclerotinia sclerotiorum, and pyra- Sclerotiniaceae (Coprotinia minutula) and two species in the Rut- midalis, each of which colonizes plants from more than 30 families. stroemiaceae (Rutstroemia cunicularia and Rutstroemia cuniculi) Each of these species belongs to a clearly distinct phylogenetic group reported as nonpathogenic to plants that are coprophilous (Elliott, with a majority of species infecting a single host family. This may 1967). Most species in the Sclerotiniaceae are reported necrotrophic result from radiation following host jumps (Choi & Thines, 2015) and pathogens, such as Botrytis, Monilinia and Sclerotinia species. Excep- suggests that the ability to colonize a broad range of plant was tions include Ciborinia whetzelii which was reported as an obligate acquired multiple times independently through the evolution of the biotroph of poplar (Andrew et al., 2012) and Myriosclerotinia species Sclerotiniaceae. that specifically infect monocot families as facultative biotrophs or symptomless endophytes (Andrew et al., 2012). The most frequently 3.2 | Two major diversification rate shifts in the parasitized plant group was Fabids (by 47 Sclerotiniaceae species and evolution of the Sclerotiniaceae 26 Rutstroemiaceae species), a group of the Rosids (Eudicots) includ- ing notably cultivated plants from the Fabales (legumes) and Rosales To test for a relationship between host range variation and biological (rose, strawberry, ...) orders. Plants from the order Vitales (e.g., diversification in the Sclerotiniaceae, we estimated divergence times grapevine), from the Magnoliids and from the Polypodiidae (ferns) for the fungal family Sclerotiniaceae using the ITS marker in a Baye- were colonized by Sclerotiniaceae but not Rutstroemiaceae fungi. sian framework. A calibrated maximum clade credibility chronogram We next used the internal transcribed spacer (ITS) region of from these analyses is shown in Figure 2a (and Figure S6). This rDNA sequences to construct a phylogenetic tree of the 105 Sclero- showed that Sclerotiniaceae fungi shared a most recent common tiniaceae and 56 Rutstroemiaceae species (Figure 1b, Files S1–S7). ancestor between 33.9 and 103.5 million years ago (Mya), with a This marker is the only one available for all species and is currently mean age of 69.7 Mya. The divergence of Botrytis pseudocinerea, considered as the universal marker for taxonomic use (Schoch et al., estimated from the ITS data set, occurred ca. 3.35–17.8 Mya (mean 2012). Maximum-likelihood, neighbour-joining, parsimony and Baye- age 9.8 Mya) and is similar to a previous estimate of 7–18 Mya sian analyses yielded convergent tree topologies. The tree topologies (Walker et al., 2011). confirmed the familial classification recognized in previous phyloge- Using this time-calibrated phylogeny, we calculated speciation nies (Andrew et al., 2012; Holst-Jensen et al., 1997, 1998), identify- and diversification rates using three different methods, to control for ing a clearly supported Botrytis genus including 23 species; a the limits of individual methods (Rabosky, Mitchell, & Chang, 2017). Myriosclerotinia genus including eight species; a Sclerotinia genus First, we used the BAMM framework, which implements a Metropo- sensu stricto including four Sclerotinia; and a Monilinia genus includ- lis Coupled Markov chain Monte Carlo method to calculate diversifi- ing 16 Monilinia species and three other species. Botrytis calthae and cation rates along lineages (Rabosky, 2014) (Figures 2a and 7a–c).

Amphobotrys ricini were the only two species with imprecise posi- The analysis identified two significant rate shifts (s1 and s2, account- tions in maximum-likelihood reconstructions, due to the ITS marker ing for >30% of the posterior distribution in the 95% credible set of being insufficient to infer complete phylogenetic placement (Loren- macro-evolutionary shift configurations) each immediately adjacent 0 0 ≤ zini & Zapparoli, 2016; Staats et al., 2005). to a minor rate shift (s1 and s2, respectively; 10% of the posterior We used RASP (Yu et al., 2015) and PHYTOOLS (Revell, 2012) to distribution) within two different clades (Figure 2a). Each shift reconstruct the ancestral host range across the phylogeny (Fig- affects a specific clade and is not detected coincidentally across the ures 1b, S1 and S5). This identified Fabids as the most likely ancestral whole phylogeny. The earliest shift occurred between 34.2 and 42.7 hosts of the Sclerotiniaceae family (relative probability of 100% in S- Mya. It resulted in an increase in instantaneous diversification rate

DIVA and S-DEC analyses). Random tree pruning in PHYTOOLS indi- from ~0.05 lineage/million year in Monilinia and related clades to cated that this result is robust to sampling biases. Over 48% of the ~0.08 lineage/million year in Sclerotinia and derived clades. The sec- Sclerotiniaceae species are pathogens of host plants that evolved prior ond shift occurred between 9.8 and 21.7 Mya. It resulted in a fur- to the divergence of the Fabids, suggesting numerous host jumps in ther increase in instantaneous diversification to >0.15 lineage/million this family of parasites. Notably, a jump to Malvids and then to year in Botrytis lineage. These two shifts defined three distinct Monocots was identify at the base of the Botrytis genus (87% and macro-evolutionary regimes in the Sclerotiniaceae. Second, to deter- 85% probability in S-DIVA, respectively), a jump to Commelinids mine how speciation rates changed over time, we computed lineage- occurred in the Myriosclerotinia genus (91% probability in S-DIVA), a through-time plots for the Sclerotiniaceae tree and 1,000 trees in jump to the occurred at the base of the Sclerotinia which we altered all branching times randomly by 15% to +15% to genus (76% probability in S-DIVA); a jump to Asterids was found at control for the sensitivity to divergence time estimates (Figure 3a). the base of a major group of Monilinia (89% probability in S-DIVA). We observed an exponential trend of species accumulation consis- A total of 73 Sclerotiniaceae species (69.5%) and 41 (73.2%) Rut- tent with a late burst of cladogenesis or early extinction (Pybus stroemiaceae infected a single host family (Figure 1c, Table S1). c = 0.284). Third, we used MEDUSA (Alfaro et al., 2009; Drummond, Moellerodiscus lentus was the only Rutstroemiaceae species infecting Eastwood, et al., 2012) to detect shifts in diversification rate in the hosts from more than five plant families whereas eleven Sclerotiniaceae phylogeny. MEDUSA reported two shifts, 6 | NAVAUD ET AL.

(a) Number of parasite species: (b) 1 0 20406080 5 10 Non pathogenic Sclerotiniaceae 15 Polypodiidae Rutstroemiaceae 20 Pinidae

Commelinids 25 other monocots Magnoliidae 30 Botrytis Ranunculales 0.87 0.88 0.52 Vitales 35 0.92 Malvids 0.99 0.91 Fabids Number of host families: Myriosclerotinia 40 Asterids 170 0.82 0.93 0.61 Lamiids 32 0.97 45 Campanulids 0.98.98 5 0.920 1

endophyte / biotroph 50 necrotroph 160 0.90 0.96 0.61 55 0.96 Sclerotiniaceae 0.68 Sclerotinia (c) 0.81 155 Sclerotiniaceae 0.72 60 Rutstroemiaceae 0.87 1.00 120 150 1.00 0.99 65 0.65 100 145 0.98 0.95 Rutstroemiaceae 0.53 70 0.31 0.55 80 0.890 140 75 0.90 1.00 0.980 60 5 1.001 1.00 0.84 13 1.001 0.760.0 1.00 Monilinia 80 0.96 40 130 0.86 0.99 0.99 8 1.00 5

Number of fungal species 1.00 20 125 90

95 120

100

115 105

0 110 1 2-4 5-9 ≥10 Number of host families

FIGURE 1 Multiple independent shifts and expansions of host range in the evolution of the Sclerotiniaceae. (a) Distribution of plant hosts of parasites from the Sclerotiniaceae and Rutstroemiaceae fungi. (b) Maximum-likelihood ITS phylogeny of 105 Sclerotiniaceae and 56 Rutstroemiaceae species showing host range information and ancestral host reconstruction. Host range is shown as circles at the tips of branches, sized according to the number of host families and coloured as in (a) according to the earliest diverging plant group in host range. Numbers at the tips of branches refer to species listed in Table S1. Branch support indicated in light red for major clades corresponds to SH-aLRT (regular), bootstrap (bold) and Bayesian posterior probabilities (italics). Reconstructed ancestral host is shown as triangles at intermediate nodes when a change compared to the previous node is predicted. Endophytes and biotrophic parasites are shown with empty circles. (c) Distribution of Sclerotiniaceae and Rutstroemiaceae species according to their number of host families

0 corresponding to shifts s1 and s2 identified in BAMM (Figure S3d). the phylogeny of the Sclerotiniaceae (Figure S4a,b). Comparison to We used a bootstrap approach to test for the sensitivity of these randomly bifurcating trees suggested that these modalities are signif- shifts to sampling richness, tree completeness and dating accuracy icant (BIC ratio 0.09). The modalities identified by RPANDA k-means 0 (Figure 3b, Figure S3). Shift s2 was detected in 100% of the boot- clustering algorithm overlapped with the three macro-evolutionary straps. Shift s1 was the second most frequent shift detected in our regimes identified previously (Figure S4c). Overall, these analyses bootstrap analysis although it appeared sensitive to sampling rich- converged towards the identification of three macro-evolutionary ness in particular. Estimates of speciation rates in MEDUSA were regimes in the Sclerotiniaceae: species in regime G1 are early diverg- robust to sampling richness, tree completeness and dating accuracy ing and showed low diversification rates, and they encompassed (r = .038 in G1, r = .074 in G2 and r = .31 in G3). These values were notably Monilinia, and some species. Regime G2 had consistent with diversification rate estimated in BAMM for each intermediate diversification rates and encompassed notably regime (Figure 3c). Fourth, we used model-free analysis of branching Myriosclerotinia species and most Sclerotinia species. Regime G3 cor- patterns in phylogenetic trees with R-PANDA (Morlon et al., 2016). responding to Botrytis genus was the most recently diverged and The spectrum of eigenvalues suggested eight modes of division in presented the highest diversification rates. NAVAUD ET AL. | 7

TABLE 1 Results of the cophylogeny analyses under CoRe-PA optimized cost settings using various methods and host association sets

Event frequency (% of host associations)

Method Host associationsa Cospeciation Sorting/Loss Duplication Host switch FD Cost/p-valb CoRe-PAc Full set (263) 15.82 2.34 15.90 4.80 34.35 3.35 33.94 3.49 38.21 0.79 Simplified (121) 9.63 2.63 11.09 4.97 40.20 3.25 39.07 4.00 40.44 0.81 G1 only (68) 15.75 3.19 16.32 7.35 37.78 4.67 30.14 5.23 13.95 0.44 G2 only (130) 17.03 3.57 16.21 7.58 33.16 5.27 33.60 5.63 14.57 0.47 G3 only (65) 11.78 5.17 14.38 8.69 32.86 6.75 40.98 6.87 8.95 0.34 PACod Full set (263) 20.91 48.29 30.79 p < 1.005 Simplified (121) 15.70 57.02 27.27 p < 1.005 G1 only (68) 27.94 55.88 16.18 p = 2.005 G2 only (130) 6.15 71.54 22.31 p = .00045 G3 only (65) 0.00 66.15 33.85 p = .00607 Jane 4 Full set (263) 0.86 67.73 8.00 3.57 19.83 1,291 (p < .01) Simplified (121) 2.90 65.22 14.78 11.59 5.51 719.47 (p < .01) G1 simple (43) 1.39 41.67 25.00 26.39 5.56 206.98 (p < .01) G2 simple (54) 2.28 75.80 11.87 3.65 6.39 374.08 (p < .01) G3 simple (24) 9.68 25.81 19.35 41.94 3.23 105.41 (p = .13) aRefers to the set of host association tested for cophylogeny: “Full set” corresponds to the complete list of all plant-Sclerotiniaceae associations; “Simpli- fied” corresponds to a reduced set covering the whole Sclerotiniaceae family; “G1,”“G2” and “G3” corresponds to associations involving Sclerotiniaceae species from macro-evolutionary regime G1, G2 or G3 only. bValues correspond to CoRe-Pa total reconstruction costs, p-value of the observed host–parasite association matrix m² in 100,000 permutations with PACo, or the p-value of the observed reconstruction cost in 100 random tip mappings in Jane 4. FD, “failure to diverge,” corresponding to events when a host speciates and the parasite remains on both new host species. cStandard deviation corresponds to frequencies calculated for 1,000 reconstruction with randomized host–parasite associations. Associations are classi- fied as “cospeciation” when speciation of host and pathogen occurs simultaneously; “duplication” when pathogen speciation occurs independently of host speciation; “sorting or loss” when a pathogen remains associated with a single descendant host species after host speciation; and “host switch” when a pathogen changes host independently of speciation events. dIn PACo taxon jackknifing, associations that contributed significantly and positively to cophylogeny were classified as “cospeciation,” significantly and negatively as “host switch” and associations with no significant contribution to cophylogeny are classified as either sorting/loss or duplication.

We compared the proportion of broad host range (≥5 plant fami- (Donoghue & Edwards, 2014; Nurk,€ Uribe-Convers, Gehrke, Tank, & lies) fungal species that emerged in the Rutstroemiaceae, in the Blattner, 2015; Zachos, Pagani, Sloan, Thomas, & Billups, 2001). We Sclerotiniaceae, and under each the three macro-evolutionary regimes hypothesized that modifications in the distribution of plants could in the Sclerotiniaceae (Figure S2b). We randomly permuted host have an impact on host association patterns in Sclerotiniaceae para- range values across the tree 10,000 times to estimate the p-values sites. To test this hypothesis, we performed host–parasite cophy- of these proportions occurring by chance. We counted one (1.8%, logeny reconstructions using CoRe-PA (Merkle et al., 2010), PACo p = .9959) broad host range species in the Rutstroemiaceae (Balbuena et al., 2013) and Jane 4 (Conow et al., 2010) (Table 1). (Moellerodiscus lentus) and eleven (10.4%, p = .0359) in the Sclerotini- We found that hosts of Sclerotiniaceae fungi include ~1,800 plant aceae. In the Sclerotiniaceae, regime G2 with intermediate diversifica- species. To consider this overall host diversity in a cophylogenetic tion rates showed the highest proportion of broad host range analysis, we used a tree including 56 plant families, a tree of 102 species (16.2%, p = .0163), followed by regime G3 (8.7% of broad Sclerotiniaceae parasitic species, and 263 host–parasite interactions host range species, p = .537) and regime G1 (5.1% of broad host (File S3). The resulting tanglegram (Figure 4a) highlighted clear para- range species, p = .8407). These analyses suggest that the evolution- site duplications notably on Ericaceae and Rosaceae in the G1 group ary history of regime G2 could have favoured the emergence of of the Sclerotiniaceae (Monilinia species). There was no obvious topo- broad host range parasites in the Sclerotiniaceae. logical congruence between the plant tree and groups G2 and G3 of the Sclerotiniaceae. To take into account sampling bias, we per- formed cophylogeny reconstructions using the full set of 263 host– 3.3 | Diversification rate shifts associate with parasite associations and a simplified set of 121 associations. Recon- variations in rates of cospeciation, duplication and structions were also performed independently on each of the three host jump in the Sclerotiniaceae macro-evolutionary regimes. CoRe-PA classifies host–pathogen asso- Within the last 50 Ma, the world has experienced an overall ciations into (i) cospeciation, when speciation of host and pathogen decrease in mean temperatures but with important fluctuations that occurs simultaneously, (ii) duplication, when pathogen speciation dramatically modified the global distribution of land plants occurs independently of host speciation, (iii) sorting or loss, when a 8 | Number of host families: <5 ; 5-9 ; ≥10NAVAUD ET AL. Botrytis elliptica (a) Botrytis prunorum Botrytis sinoallii Diversification rate Botrytis caroliniana Botrytis fabiopsis 0.04 0.11 0.17 0.25 Botrytis byssoidea Botrytis tulipae Botrytis ficariarum Botrytis sphaerosperma G3 Botrytis porri Botrytis hyacinthi Botrytis convoluta Botrytis paeoniae Ciboria aestivalis Botrytis aclada 0.02 (b) 0.1 Botrytis cinerea 16 0.29 Botryotinia narcissicola s’ Botrytis sinoviticola 2 Botrytis pseudocinerea 14 Ciboriopsis gemmigera s Botrytis pelargonii 12 2 Myriosclerotinia sulcatula 0.04 Myriosclerotinia scirpicola Myriosclerotinia duriaeana 10 0.53 Myriosclerotinia curreyana Myriosclerotinia caricis-ampullaceae 8 Myriosclerotinia dennisii Myriosclerotinia luzulae

in group Myriosclerotinia ciborium 6 Ciborinia whetzelii Ciborinia foliicola 4 0.84 Encoelia pruinosa Monilinia johnsonii 0.99 2 Monilinia polystroma

% of broad host range (≥5 families) % of broad 0 Elliottinia kerneri All G1 G2 G3 Ciboria caucus Sclerotinia sp. CBS115977 Rut. Sclerotiniaceae Ciborinia camelliae G2 Coprotinia minutula Ciboria viridifusca Ciboria americana Stromatinia cryptomeriae Pycnopeziza sympodialis Cristulariella depraedans Sclerotinia sp.2 Hinomyces pruni Grovesinia pyramidalis 0.13 Kohninia linnaeicola Sclerotinia nivalis s cepivorum 1 Sclerotium perniciosum ulmariae 0.052 Dumontinia tuberosa Sclerotinia minor s’ Sclerotinia sclerotiorum 1 Sclerotinia trifoliorum Sclerotinia subarctica Sclerotinia glacialis Sclerotinia tetraspora Botrytis calthae Stromatinia rapulum Sclerotium denigrans Pycnopeziza sejournei Haradamyces foliicola Ciboria batschiana Monilinia polycodii Monilinia baccarum Monilinia gaylussaciae Monilinia oxycocci Monilinia urnula Monilinia megalospora Monilinia jezoensis Monilinia seaverii Monilinia azaleae Monilinia cassiopes Sclerotinia pirolae Monilinia ssiori Monilinia padi Monilinia aucupariae Monilinia linhartiana Monilinia amelanchieris G1 Ciborinia erythronii Ciboria betulae Ciboria cistophila Streptobotrys caulophylli Streptobotrys streptothrix Mycopappus alni Ciboria conformata azaleae 2 Amphobotrys ricini Encoelia fascicularis Encoelia sp. Ciborinia allii Ciboria acerina heterodoxa

70 60 50 40 30 20 10 0 Time before present (Mya) Plei. Plio. Upper Paleocene Eocene Oligocene Miocene Holo.

Cretaceous Paleogene Neogene Quat.

FIGURE 2 Two major diversification rate shifts in the evolution of the Sclerotiniaceae. (a) Dated ITS-based species tree for the Sclerotiniaceae with diversification rate estimates. The divergence times correspond to the mean posterior estimate of their age in millions of years calculated with BEAST. Mean age is shown for selected nodes with bars showing 95% confidence interval of the highest posterior density (HPD). Branches of the tree are colour-coded according to diversification rates determined with BAMM. Major rate shifts identified in BAMM 0 0 are shown as red circles, noted s1, s1, s2 and s2 and labelled with the posterior distribution in the 95% credible set of macro-evolutionary shift configurations. Species names are shown in black if host range includes less than five plant families, in yellow for five to nine plant families and in red for 10 or more plant families. Diversification rate shifts define three macro-evolutionary regimes noted G1, G2 and G3 and boxed in blue, grey and brown, respectively. (b) Distribution of broad host range (five or more host families) parasites in Rutstroemiaceae, Sclerotiniaceae and under each macro-evolutionary regime of the Sclerotiniaceae. p-Values calculated by random permutations of host ranges along the tree are indicated above bars. Holo., Holocene; Mya, Million years ago; Plei., Pleistocene; Plio., Pliocene; Quat. Quaternary; Rut., Rutstroemiaceae NAVAUD ET AL. | 9

(a) (b) 0.30

0.25

0.20 60 80 100

0.15 40

Number of species 0.10 γ = 0.284 net diversification rate net diversification 0.05

020 0.00 −70 −60 −50 −40 −30 −20 −10 0 –60 –50 –40 –30 –20 –10 0 Time Time

(b) Sampling richnessTree completeness Dating accuracy

r = 0.302 ± 0.009 r = 0.308 ± 0.007 r = 0.320 ± 0.068 n = 100 n = 100 n = 100

r = 0.074 ± 0.001 r = 0.072 ± 0.004 r = 0.075 ± 0.007 = 41 n = 28 n n = 54

r = 0.028 ± 0.005 r = 0.043 ± 0.007 r = 0.042 ± 0.007 n = 100 n = 100 n = 100

0.0 0.1 0.2 0.3 0.4 0.05 0.10 0.15 0.20 0.25 0.30 0.05 0.10 0.15 0.20 0.25 0.30

FIGURE 3 Robustness of diversification rate shifts identification in the Sclerotiniaceae phylogeny. (a) Lineage-through-time plots for the Sclerotiniaceae tree and 1,000 trees in which branching times were altered randomly by 15% to +15% to control for the sensitivity to divergence time estimates. Pybus c for the Sclerotiniaceae tree is provided. (b) Frequency (n) of diversification rate shift detection and diversification rate estimates (r) in a 100 MEDUSA bootstrap replicates in which sampling richness, tree completeness and divergence times were randomly altered. Labels indicate average diversification rate estimates (r) for each macro-evolutionary regime (blue for G1, grey for G2, brown for G3), with standard deviation of the mean for a 100 replicates. (c) Net diversification rates over time estimated by BAMM for each macro-evolutionary regime (blue for G1, grey for G2, brown for G3)

pathogen remains associated with a single descendant host species taxon jackknifing to test for the relative contribution of each host– after host speciation, and (iv) host switch (also designated as host pathogen association in the cophylogeny pattern. In the full set of jump) when a pathogen changes host independently of speciation host–Sclerotiniaceae associations, ~21% contributed positively and events (Merkle et al., 2010). The CoRe-PA reconstructions indicated significantly to cophylogeny, likely representing cospeciation events, that duplications and host switch each represented ~34% of host while ~31% contributed negatively and significantly to cophylogeny, associations from the full set of host–Sclerotiniaceae associations. therefore likely representing host jump events (Table 1). Taxon jack- Cospeciation and sorting each represented ~16% of host associa- knifing on individual macro-evolutionary regime revealed a high fre- tions. The analysis of each macro-evolutionary regime indicated that quency of associations with positive contribution to cophylogeny G1 is characterized by high duplication and low host jump frequen- (likely cospeciation) in G1, and a high frequency of associations with cies and G3 by low cospeciation and sorting frequencies and high negative contribution to cophylogeny (likely host switch) in G3, in host jump frequencies (Figure 4b). We next used Procrustean super- good agreement with the CoRe-PA analysis. In addition to the types imposition in PACo (Balbuena et al., 2013) to identify host–pathogen of host–parasite association mentioned before, Jane 4 identifies “fail- associations that contribute significantly to cophylogeny between ure to diverge” associations, corresponding to events when a host Sclerotiniaceae and host plant families. The PACo analysis suggested speciates and the parasite remains on both new host species (Conow that overall, the Sclerotiniaceae lineages are not randomly associated et al., 2010). Jane 4 analysis for the whole Sclerotiniaceae family with their host families (p < .01). The PACo analysis also includes (simplified set of associations) identifies losses as the dominant form 10 | NAVAUD ET AL. of host association (~65%), followed by duplications (~15%) and host regime G2 to G3. Our ancestral state reconstruction analysis jumps (~12%) while failure to diverge (~5.5%) and cospeciation (~3%) inferred a jump to monocots at the base of regime G3 (Figure 1). was rare event (Table 1). Consistent with CoRe-PA and PACo analy- A recent study on Hesperiidae butterflies reported a strong increase ses, Jane 4 found the highest rate of host jumps in regime G3 in diversification coincident with a switch from dicot-feeders to (~42%). Unlike previous analyses, Jane 4 found the highest rate of monocot-feeders (Sahoo, Warren, Collins, & Kodandaramaiah, cospeciation in G3 (~9.7%). G1 was characterized by a high duplica- 2017). As postulated for Hesperiidae, the emergence of open grass- tion rate, while G2 was characterized by a high rate of losses and lands and global temperature decrease may have affected diversifi- failure to diverge events but the lowest host jump rate. Overall, our cation in the Sclerotiniaceae. cophylogeny analyses converged towards the conclusion that cospe- Competition for resources is likely to lead to speciation if host ciations represent a minor proportion of host associations in the range expansion is costly (Ackermann, Doebeli, & Gomulkiewicz, Sclerotiniaceae and that the proportion of host jumps varied mark- 2004). Notably, if different host families are scattered in space and edly between the three macro-evolutionary regimes, with G3 show- phylogenetically related hosts are clustered, the cost of host range ing the highest host jump rate (between 33% and 42%). expansion is expected to increase, due to higher costs for dispersal or trade-offs with other traits (Ackermann et al., 2004). In agreement with this theory, genomic signatures associated with metabolic cost 4 | DISCUSSION optimization were stronger in generalist than specialist fungal para- sites (Badet et al., 2017). Strong climate oscillations during the Ceno- Our analyses lead to a model in which the extant diversity of zoic Era, leading to population isolation through range fragmentations Sclerotiniaceae fungi is the result of three macro-evolutionary and dispersal events, likely contributed to the radiation of plant lin- regimes characterized by distinct diversification rates and host eages (Nyman, Linder, Pena,~ Malm, & Wahlberg, 2012). For instance, association patterns. Patterns of cophylogeny decreased from major events in the diversification of Brassicaceae plant family coin- regime G1 to G3, while the frequency of inferred host jump events cide with glaciations and arid conditions of the Eocene–Oligocene increased from G1 to G3. Regime G2, which includes the highest and the Oligocene–Miocene transitions (Hohmann, Wolf, Lysak, & proportion of broad host range parasites, showed a frequency of Koch, 2015). In addition, the mid-Miocene (20–10 Mya) corresponds host jumps intermediate between G1 and G3. Our cophylogeny to the emergence of the first open grassland habitats in northern Eur- analyses are consistent with the view that long-term plant-patho- asia (Stromberg,€ 2011). The fragmentation and diversification of plant gen cospeciation is rare (Devienne et al., 2013). The decrease in host populations may have increased the cost for host range expan- host-pathogen cophylogeny signal from G1 to G3 regime (Figure 4) sion in fungal pathogens. A jump to monocots at the base of Sclero- could indicate more frequent true cospeciation events in early tiniaceae regime G3 ~10 Mya may have favoured the conquest of diverging Sclerotiniaceae species or could result from host jumps grassland habitats and the rapid diversification of specialist species being restricted to closely related hosts for G1 species whereas G2 over the emergence of generalists in this group. and G3 species progressively gained the ability to jump to more Similar to herbivore diet breadth (Forister et al., 2015), host divergent hosts (Devienne, Giraud, & Shykoff, 2007; Devienne range in the Sclerotiniaceae shows a continuous distribution from et al., 2013). Low diversification rates in regimes G1 and G2 may specialists to broad host range generalists, with the majority of spe- have resulted from increased extinction rates. It is conceivable that cies being specialists. Mathematical analyses and studies of the host a high extinction rate of specialist parasites during regime G2 is range of insect herbivores suggest that host range expansion could responsible for the reduced clade diversification rates, while involve the emergence of a “pre-adaptation” followed by the colo- increasing the apparent frequency of emergence of generalist spe- nization of new hosts (Janz & Nylin, 2008). The existence of such cies. This phenomenon, consistent with the view of specialization pre-existing enabler traits has been proposed as a facilitator for sev- as “an evolutionary dead end” (leading to a reduced capacity to eral shifts in plant species distribution (Donoghue & Edwards, 2014). diversify), is notably supported in Tachinidae parasitic flies and Notably, the pre-existence of a symbiotic signalling pathway in algae hawkmoths pollinating Ruellia plants (Day et al., 2016). An analysis is thought to have facilitated the association of land plants with of Papilionoidea and Heliconii revealed lower rates of diversification symbiotic fungi (Delaux et al., 2015). In the plant genus Hypericum, for butterfly species feeding on a broad range of plants compared increased diversification rates in the Miocene Epoch were likely facil- to specialist butterflies (Day et al., 2016; Hardy & Otto, 2014). itated by adaptation to colder climates (Nurk€ et al., 2015). In the Consistently, theoretical models of sympatric speciation predict that bacterial pathogen Serratia marcescens and in yeast, adaptation to competition for a narrow range of resources (specialization) to be a temperature change was associated with improved tolerance to strong driver of speciation (Dieckmann & Doebeli, 1999). Similar to other stresses (Caspeta & Nielsen, 2015; Ketola et al., 2013). Analy- plant-feeding insects, the diversity of fungal and oomycete patho- sis of the complete predicted proteomes of S. borealis, S. sclerotiorum gens is considered largely driven by host jumps rather than host and Botrytis cinerea has revealed protein signatures often associated specialization that may follow (Choi & Thines, 2015; Hardy & Otto, with cold adaptation in the secreted protein of all three species 2014). Low diversification rates in G1 and G2 may also result from (Badet, Peyraud, & Raffaele, 2015). Cold tolerance might have been a strong increase in diversification rate during the transition from a pre-adaptation that facilitated the emergence of generalist NAVAUD ET AL. | 11

(a) Hosts (59 families)Associations (263) Parasites (102 species)

Phytolaccaceae Valdensinia heterodoxa Encoelia sp HB2015 Ericaceae Encoelia fascicularis Amphobotrys ricini 1 Primulaceae Amphobotrys ricini 2 Ciboria acerina Theaceae Ciborinia allii Oleaceae Ciboria conformata Lamiaceae Monilinia megalospora Monilinia urnula Plantaginaceae Monilinia oxycocci Monilinia gaylussaciae Convolvulaceae Monilinia baccarum G1 Monilinia polycodii Solanaceae Sclerotinia pirolae Apocynaceae Monilinia cassiopes Monilinia azaleae Caprifoliaceae Monilinia jezoensis Monilinia seaverii Asteraceae Monilinia mali Monilinia amelanchieris Campanulaceae Monilinia linhartiana Monilinia aucupariae Apiaceae Monilinia padi Araliaceae Monilinia ssiori Ciborinia erythronii Cornaceae Ciboria betulae Ciboria cistophila Oxalidaceae Sclerotinia bulborum Mycopappus alni Hypericaceae Streptobotrys caulophylli Euphorbiaceae Streptobotrys streptothrix Botrytis calthae Salicaceae Sclerotinia tetraspora Kohninia linnaeicola Celastraceae Sclerotinia nivalis Grovesinia pyramidalis Rosaceae Hinomyces pruni Moraceae Ciboria amentacea Dumontinia ulmariae Myricaceae Dumontinia tuberosa Sclerotium cepivorum Betulaceae Sclerotium perniciosum Sclerotinia subarctica Fagaceae Sclerotinia trifoliorum Sclerotinia sclerotiorum Fabaceae Sclerotinia minor Cucurbitaceae Sclerotinia glacialis Sclerotinia sp2 Sapindaceae Pycnopeziza sympodialis Cristulariella depraedans G2 Rutaceae Ciboria viridifusca Ciboria americana Cistaceae Ciborinia camelliae Malvaceae Ciboria caucus Sclerotinia sp CBS115977 Brassicaceae Encoelia pruinosa Ciborinia foliicola Combretaceae Ciborinia whetzelii Monilinia johnsonii Myrtaceae Monilinia polystroma Monilinia fructigena Geraniaceae Monilinia fructicola Vitaceae Monilinia laxa Myriosclerotinia luzulae Paeoniaceae Myriosclerotinia dennisii Myriosclerotinia curreyana Proteaceae Myriosclerotinia caricis Myriosclerotinia duriaeana Menispermaceae Myriosclerotinia scirpicola Berberidaceae Myriosclerotinia sulcatula Myriosclerotinia ciborium Ciboriopsis gemmigera Botrytis pelargonii Lauraceae Botrytis pseudocinerea Botrytis sinoviticola Magnoliaceae Botrytis fabae G3 Araceae Botrytis cinerea Botryotinia narcissicola Commelinaceae Botrytis ficariarum Botrytis fabiopsis Juncaceae Botrytis caroliniana Botrytis prunorum Cyperaceae Botrytis sinoallii Botrytis byssoidea Arecaceae Botrytis tulipae Ciboria aestivalis Orchidaceae Botrytis paeoniae Botrytis convoluta Iridaceae Botrytis squamosa Botrytis hyacinthi Xanthorrhoeaceae Botrytis porri Amaryllidaceae Botrytis sphaerosperma Botrytis aclada Asparagaceae Ciboria batschiana Haradamyces foliicola Liliaceae Pycnopeziza sejournei Sclerotium denigrans Smilacaceae Stromatinia rapulum Colchicaceae Sclerotinia borealis

(b) ** ** (c) 50 100 20 ** 90 18 40 80 16 70 14 ** 30 60 12 50 10 20 40 8 30 6 20 4 10 % of host–parasite associations % of host–parasite 10 2 % of species with ≥ 5 host families 0 0 % of host–parasite associations 0 All G1 G2 G3

cospeciation sorting/loss duplication host switch contribution to cophylogeny:

All Sclerotiniaceae G1 G2 G3 Positive nonsignificant Negative

FIGURE 4 Diversification rate shifts associate with variations in rates of cospeciation, duplication and host switch in Sclerotiniaceae fungi. (a) Tanglegram depicting the associations between 102 Sclerotiniaceae species and 59 plant families. The three macro-evolutionary regimes are indicated by coloured boxes on the Sclerotiniaceae tree. Fungal species labels are colour-coded as in Figure 2. (b) Proportion of cospeciation, sorting/loss, duplication and host switches in host–Sclerotiniaceae associations as predicted by CoRe-PA in 1,000 cophylogeny reconstructions. ** indicate large effect size in a macro-evolutionary compared to the complete set of associations as assessed by Cohen’s d test. (c) Proportion of host–Sclerotiniaceae associations contributing significantly and positively (likely cospeciation), non significantly and significantly and negatively (likely host switch) to cophylogeny in PACo analysis. The black dotted line indicates the percentage of broad host range species (five or more host families) in each group parasites under low diversification rates (regime G2) and a rapid structures and host–parasite association patterns. Knowledge on the diversification following host jumps on fragmented host populations dynamics of pathogen evolution increases the understanding of the (regime G3). Indeed, Sclerotinia borealis, S. glacialis, S. subarctica and complex interplay between host, pathogen and environment govern- S. nivalis, that diverged during regime G2, are largely restricted to ing the dynamics of disease epidemics. These evolutionary principles hemiboreal climates (circumboreal region) and can have a lower opti- are useful for the design of disease management strategies (Vander mal growth temperature than their sister species (Hoshino, Terami, Wal et al., 2014) and provide new insights into the factors that influ- Tkachenko, Tojo, & Matsumoto, 2010; Saito, 1997). enced the diversity of extant fungal parasite species. These findings suggest that global climate instability and host diversification in the Cenozoic might have impacted on the diversity ACKNOWLEDGEMENTS of fungal parasites within the Sclerotiniaceae. This effect could have been direct, through the emergence of cold adaptation as an This work was supported by grants from the European Research enabling trait, or indirect through changes in host population Council (ERC-StG-336808 to S.R.), the French Laboratory of 12 | NAVAUD ET AL.

Excellence project TULIP (ANR-10-LABX-41; ANR-11-IDEX-0002- Barrett, L. G., Kniskern, J. M., Bodenhausen, N., Zhang, W., & Bergelson, – 02) and a “New Frontiers” grant of the Laboratory of Excellence pro- J. (2009). Continua of specificity and virulence in plant host patho- gen interactions: Causes and consequences. New Phytologist, 183, ject TULIP. We thank the TULIP international advisory board and 513–529. https://doi.org/10.1111/j.1469-8137.2009.02927.x the TULIP community for stimulating discussions. Beimforde, C., Feldberg, K., Nylinder, S., Rikkinen, J., Tuovila, H., Dorfelt,€ H., ... Schmidt, A. R. (2014). Estimating the Phanerozoic history of the lineages: Combining fossil and molecular data. Molec- DATA ACCESSIBILITY ular phylogenetics and evolution, 78, 386–398. https://doi.org/10. 1016/j.ympev.2014.04.024 Boland, G., & Hall, R. (1994). Index of plant hosts of Sclerotinia sclerotio- • Species identifiers: GenBank accessions provided in Table S1 rum. 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